Project Summary We have collected, under our parent award (1R01CA257814-01), a database of serial multi-parametric magnetic resonance (MR) images as well as patient-reported and objective toxicity measures for more than 400 head and neck (HNC) patients, at pre-, on-, and post-therapy. We plan to utilize this data, in complete alignment with the first specific aim of the parent award, to effectively quantify treatment-related response on tumor/node and normal tissue in order to develop personalized treatment planning adaptations for individual HNC patients. As the most data-rich image toxicity cohort to the best of our knowledge, however, this database necessitates rigorous curation to be utilized for artificial intelligence/machine learning (AI/ML) approaches to predict, for example, tumor complication probability (TCP) and normal tissue complication probability (NTCP). Specifically, multi-observer segmentation of tumor and normal tissue regions of interest is required. Additionally, dissemination efforts are necessary to engage experts from AI/ML communities to develop AI/ML-approaches for auto-segmentation models, and TCP/NTCP predictions. To this end, we plan to undertake three specific aims. Through our first specific aim, we plan to curate our serial multi-parametric, multi time-point MRI dataset (accompanied with extracted radiomics) for therapeutic response and TCP prediction through assembling a team of three physicians to obtain the ground-truth segmented images. We further plan to deposit the curated segmented images as a dataset to The Cancer Imaging Archive (TCIA). As our second specific aim, we plan for curation and public deposition of matched image-dose multi-time-point acute and late toxicity metrics to be disseminated to both AI/ML experts for NTCP modeling. We will particularly include patient-reported MD Anderson Symptom Inventory-Head and Neck (MDASI-HN) toxicity outcomes, Common Toxicity Criteria- Adverse Events (CTC-AE) physician-ranked toxicity, and objective measures of swallowing dysfunction such as modified barium swallowing and tube-feeding assessments. In the third specific aim, we plan to design and execute a public crowdsourced challenge for serial image dose- response prediction for both TCP and NTCP prediction modeling tasks. Based on the test dataset that we plan to release after the execution of the challenge, we will conduct a post-challenge analysis on the submitted models (e.g., false-positive, and false-negative cases), and disseminate the best results as manuscripts to be submitted for publications and presentations. If successful, the proposed efforts are directly responsive to the need for AI/ML-ready datasets to be utilized for cancer treatment.